11/22/2021

Meet the sea otters: Enhydra lutris

sea otter

  • Cute! and Furry!
  • In fact … the furriest: 150,000 hairs / cm2 (>1,000,000 / in2)

Sea otters: keystone species

sea otter keystone

(Estes et al. 1974)

Sea otters: Keystone

sea otter keystone

(Smith et al. 2021)

Sea otters: Range

sea otter range

  • Entire (littorally) North Pacific

Sea otters: Furriness > Cuteness

fur trade

  • Fur trade (Russian -> British -> American) leads to near extirpation across the entire range.

  • 300,000 in 1740 … < 2,000 in 1900.

  • Displacement and indenturing of Indigeneous fishermen (esp. Aleut)

fur trade

… the rush for the otters’ “soft gold” was a predictable boom and bust cycle, a cautionary example of unsustainable resource use, and a socioeconomic driver of Western—mainly American—involvement in the Pacific region starting in the eighteenth century. (Loshbaugh 2021)

Sea otters: Culturally significant

Sea otters: not totemic

fur trade fur trade

  • Ainu - Esaman
  • Aleut - Chaxtux̂
  • Haida - Ku
  • Tlingit - Yáxwchʼ
  • Siletz - Elakha

Sea otter reintroduction: Pacific NW

Remnant populations from Aleutian Islands … released in OR, WA, BC and SE-AK 1969 – 1972.

Reintroduction trade Reintroduction trade

Sea otter reintroduction: Washington State …

Population ecology is all about …

\(\huge N\)
  • but where? when?

Here! Now! …

\(\huge N_t\)
  • but how many were there?

That many, then (\(\Delta t\) ago)!

\(\Large N_t = N_{t - \Delta t} + \Delta N\)

How does population change?

\(\Large N_t = N_{t - \Delta t} + B - D + I - E\)

Birth

Death

Immigration

Emigration

For now … no one’s getting on or off the bus

\(\Large N_t = N_{t - \Delta t} + B - D + I - E\)

Birth

Death

Immigration

Emigration

This is a closed population … and what we will be (mainly) dealing with for the next 3 weeks.

Sea otter data:

Plot Data:

plot(WA)

Fit a line

plot(WA); abline(lm(count ~ year, data = WA))

Plot on Log scale:

plot(WA, log = "y")

Fit linear model log-growth

WA.growth <- lm(log(count) ~ I(year-1970), data = WA)
summary(WA.growth)
## 
## Call:
## lm(formula = log(count) ~ I(year - 1970), data = WA)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.191084 -0.062944 -0.005104  0.055518  0.231704 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.082641   0.073024   55.91   <2e-16 ***
## I(year - 1970) 0.073251   0.002367   30.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1094 on 23 degrees of freedom
## Multiple R-squared:  0.9766, Adjusted R-squared:  0.9755 
## F-statistic: 958.1 on 1 and 23 DF,  p-value: < 2.2e-16
par(mfrow = c(1,2))
plot(WA.growth, 1:2)

#require(ggplot2)
#ggplot(WA, aes(year, count)) + geom_point() + geom_smooth(method = "glm", #family = "log")

A little math:

\[\log(N_i) = \alpha + \beta \, Y_i\] \[N_i = \exp(\alpha) \times \exp(\beta \, Y_i)\] \[N_i = e^\alpha {e^\beta}^{Y_i}\] \[N_i = N_0 \lambda ^ {Y_i}\]

where \(N_0 = e^{\alpha} = e^{4.08} = 59.14\), and \(\lambda = e^{\beta} = e^{0.07325} = 1.076\).

SO … percent rate of growth is about 7.6%!

Plot exponential growth

plot(count~year, data = WA)
curve(59.14 * exp(0.07325 * (x-1970)), add = TRUE, col = 2, lwd = 2)

Powers of 10:

References

  • J. A. Estes, J. F. Palmisano. 1974. Sea otters: Their role in structuring nearshore communities. Science 185, 1058–1060.
  • Smith et al. 2021. Behavioral responses across a mosaic of ecosystem states restructure a sea otter–urchin trophic cascade. PNAS Mar 2021, 118 (11)
  • Loshbaugh S. 2021. Sea Otters and the Maritime Fur Trade. In: Davis R.W., Pagano A.M. (eds) Ethology and Behavioral Ecology of Sea Otters and Polar Bears. Ethology and Behavioral Ecology of Marine Mammals.
  • Gilkinson, A.K., Pearson, H.C., Weltz, F. and Davis, R.W., 2007. Photo‐identification of sea otters using nose scars. The Journal of Wildlife Management, 71(6), pp.2045-2051.